Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

精确的生物标志物开发是疾病管理的关键步骤。然而，大多数已发表的生物标志物来自相对较少的样本，采用监督方法。无监督机器学习的最新进展有望利用非常大的数据集来更好地预测疾病生物标志物。去噪自编码器(DA) 是一种无监督的深度学习算法，它是自编码器技术的随机版本。DA 的原理是通过从损坏的输入中重建干净的输入来强制自动编码器的隐藏层捕获更健壮的特征。在这里，应用 DA 模型来分析来自 13 个已发表肺癌的综合转录组数据研究，其中包括 1916 人肺组织样本。使用 DA，我们发现了一个由多个肺腺癌 (ADC) 基因组成的分子特征。在独立的验证队列中，所提出的分子特征被证明是肺癌组织学亚型的有效分类器。此外，该特征成功地预测了肺 ADC 的临床结果，这与传统的预后因素无关。更重要的是，与其他已发表的预后基因相比，该特征表现出优越的预后能力。我们的研究表明，无监督学习有助于精准医学时代的生物标志物开发。